ProjectSummary Hospitalshaverapidlyadoptedtheuseofelectronicmedicalrecords(EMR)forroutinemanagementandreportingof patienthealthcareutilization.InspiteofthecomprehensivedatacollectedinEMRs,theyhavenotrealizedtheir potentialforconductingroutinesurveillanceofqualitymeasures,formeasuringhospitalperformance,orfor surveillanceofpatientsafety.TheuseofEMRsforpatientsafetysurveillanceandforpredictiveanalyticshasbeen underutilizedespeciallyforacutemyocardialinfarction(AMI).Reasonsforthisunderuseincludefragmentationofdata entryandstorage,poorcomplianceincompletingstructuredfieldsforqualityreporting,andtheabundanceof unstructuredinformationdescribedinnarrativenotes.Weproposetodeveloparobustautomatedsurveillancetoolkit builtintwoindependentEMRswithexternalvalidationinmultipleEMRs.Wewillcombinetherichinformationlocked inclinicalnoteswithstructureddatatoquantifytheriskforreadmissionafteranAMIdirectlyfromtheEMR,validate, anddemonstrateitsportabilityacrossinstitutionstootherEMRs.Ouroverallhypothesisisthataddingstructured variablesfromtheEMRwithNLP-derivedvariableswillimproveourabilitytopredict30-dayreadmissionfromAMI. Wewillevaluatethishypothesisbymappingrelevantvariablestocommoninformationmodels,developingand validatingpredictionmodelsforAMI,andcreatingandvalidatingaportabletoolkitforgeneratingpredictivemodels frommultipleEMRsinthefollowingspecificaims:1)ToevaluatepotentialAMIriskfactorsfor30-dayreadmission fromAMItoacommoninformationmodelusingstructuredEMRvariablesandnovelNLPvariablesextractedfrom EMRtext;?2)Todevelopanoptimalpredictionmodelfor30-dayreadmissionfromAMIateachsiteusingregistrydata, structuredEMR,andnovelsocialNLPvariablesextractedfromunstructuredEMRtextandtocross-validateeach modelatanotherinstitution;?3)Tovalidateanautomatedsurveillancetoolkit(ReX)forportabilitytothreeotherEMRs. ThisresearchissignificantinthatitwillimproveourabilitytoidentifyAMIpatientsatriskof30-dayreadmission, identifyriskforcausesofreadmissionforactionableinterventionbeforereadmissionoccurs,andforthefirsttime provideavalidatedportablesurveillancetoolkit.Ourresearchisinnovative,becauseitexpandstheuseofNLPtools tonovelvariablespreviouslyonlyobtainedthroughmanualextraction(e.g.,socialriskfactors)anddevelopsa generalizableandportabletoolkitbuiltinparallelontwoindependentEMRswithexternalvalidationinmultipleEMRs. Wewillshifttheparadigmfromcurrentsingle-centerapproachestoa2-centerparalleldevelopmentandcross- validationmethodallowingfornovelinformationevaluationandsystematicdifferencesindatarepresentationbetween thetwoinstitutionsandadaptingourportabletoolkitaccordingly.Wewillsignificantlyadvancebiomedicalinformatics tooldevelopmentandourabilitytoperformriskassessmentforAMIpatients,enablingimprovedclinicalcareand improvedpatientoutcomes.